Polygons
In most simulations, such as Global Climate Models, Unstructured Grids are composed of nodes that are connected with edges to form faces that discretize the surface of a sphere (i.e. Earth).
For visualization, these faces can be geometrically represented as Polygons and shaded by a corresponding face-centered data variable.
Setup
import uxarray as ux
file_dir = "../../meshfiles/"
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.
warnings.warn(
Conversion Methods
UXarray represents Unstructured Grids through a set of coordinate and connectivity variables (i.e. node_lon, node_lat, face_node_connectivity, etc.). These variables can be manipulated to obtain our Polygons for visualuzation
Representation as a GeoDataFrame
Polygons are stored in a SpatialPandas GeoDataFrame, which is the expected data structured by the HoloViz stack of packages for visualizing polygons.
A Grid can be converted into a GeoDataFrame, which will contain a single “geometry” column, which is a series of Polygons that represent each face.
uxds_mpas.uxgrid.to_geodataframe()
| geometry | |
|---|---|
| 0 | MultiPolygon([[[-173.4220428466797, 28.4104290... |
| 1 | MultiPolygon([[[-180.0, 87.7088242, -138.95294... |
| 2 | MultiPolygon([[[3.516157388687134, -28.4104290... |
| 3 | MultiPolygon([[[79.46817016601562, -25.8366222... |
| 4 | MultiPolygon([[[-28.531827926635742, 25.836622... |
| ... | ... |
| 1786 | MultiPolygon([[[-102.95294189453125, -50.05697... |
| 1787 | MultiPolygon([[[-102.95294189453125, -52.62263... |
| 1788 | MultiPolygon([[[-171.18515014648438, -53.84706... |
| 1789 | MultiPolygon([[[-178.7207489013672, -53.847068... |
| 1790 | MultiPolygon([[[-180.0, -53.1438933, -178.7207... |
1791 rows × 1 columns
A UxDataArray can also be converted into a GeoDataFrame. It will now have an additional column containing a 1D-slice of data variable.
It’s important to note that to convert a UxDataArray into a GeoDataFrame, the dimension of the data variable must match the number of faces (a.k.a. mapped to faces) and there can not be any additional dimensions (i.e. time, level, etc.)
uxds_mpas["bottomDepth"].to_geodataframe()
| geometry | bottomDepth | |
|---|---|---|
| 0 | MultiPolygon([[[-173.4220428466797, 28.4104290... | 4973.000000 |
| 1 | MultiPolygon([[[-180.0, 87.7088242, -138.95294... | 4123.000000 |
| 2 | MultiPolygon([[[3.516157388687134, -28.4104290... | 2639.000000 |
| 3 | MultiPolygon([[[79.46817016601562, -25.8366222... | 4001.012148 |
| 4 | MultiPolygon([[[-28.531827926635742, 25.836622... | 5403.000000 |
| ... | ... | ... |
| 1786 | MultiPolygon([[[-102.95294189453125, -50.05697... | 3945.000000 |
| 1787 | MultiPolygon([[[-102.95294189453125, -52.62263... | 4431.000000 |
| 1788 | MultiPolygon([[[-171.18515014648438, -53.84706... | 5197.000000 |
| 1789 | MultiPolygon([[[-178.7207489013672, -53.847068... | 5499.990273 |
| 1790 | MultiPolygon([[[-180.0, -53.1438933, -178.7207... | 4855.000000 |
1791 rows × 2 columns
If a data variable is not face-centered, it can be manipulated to get it to map to faces. For node-centered data, as is the case with our Geoflow dataset, we can perform a nodal-average operation, which takes the average all the nodes that saddle a face and use that value to shade the polygon.
Here we can also see that we need to index the time and meshLayers dimensions to obtain our 1D slice of data.
uxds_geoflow["v1"].nodal_average()[0][0].to_geodataframe()
| geometry | v1_nodal_average | |
|---|---|---|
| 0 | MultiPolygon([[[0.0, 58.28252410888672, 5.2137... | -0.003357 |
| 1 | MultiPolygon([[[5.213775634765625, 59.79991149... | -0.005317 |
| 2 | MultiPolygon([[[16.497974395751953, 62.0571365... | -0.009873 |
| 3 | MultiPolygon([[[29.138521194458008, 63.2698593... | -0.011219 |
| 4 | MultiPolygon([[[0.0, 61.001914978027344, 5.342... | -0.006898 |
| ... | ... | ... |
| 3835 | MultiPolygon([[[63.31489562988281, -38.6694831... | -0.047156 |
| 3836 | MultiPolygon([[[52.8786506652832, -32.05970001... | -0.513393 |
| 3837 | MultiPolygon([[[55.743770599365234, -32.611831... | -0.398253 |
| 3838 | MultiPolygon([[[61.32698440551758, -33.4846153... | -0.269892 |
| 3839 | MultiPolygon([[[67.02494812011719, -34.1040725... | -0.196878 |
3840 rows × 2 columns
Vector Polygon Plots
UXarray provides the plot.polygons() method, which renders each polygon as a vector graphic using HoloViews.
uxds_mpas["bottomDepth"].plot.polygons(width=900, height=400)
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/uxarray/plot/dataarray_plot.py:480: UserWarning: Including Antimeridian Polygons may lead to visual artifacts. It is suggested to keep 'exclude_antimeridian' set to True.
warnings.warn(
uxds_geoflow["v1"].nodal_average()[0][0].plot.polygons(
cmap="coolwarm", width=900, height=400
)
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/uxarray/plot/dataarray_plot.py:480: UserWarning: Including Antimeridian Polygons may lead to visual artifacts. It is suggested to keep 'exclude_antimeridian' set to True.
warnings.warn(
Important
For large datasets (i.e. 1,000,000 or more faces), it’s not recommended to directly plot Polygons.
Rasterized Polygon Plots
For larger datasets & quicker visualizations, we can rasterize each polygon to obtain a Raster of our geometries.
uxds_mpas["bottomDepth"].plot.rasterize(method="polygon", width=900, height=400)
uxds_geoflow["v1"].nodal_average()[0][0].plot.rasterize(
method="polygon", cmap="coolwarm", width=900, height=400
)
Handling Antimeridian Polygons
When attempting to visualize unstructured meshes that reside on a sphere, it’s necessary to consider the behavior of geometric elements near the Antimeridian. Elements that exist on or cross the antimeridian need to be corrected to properly visualize them. UXarray uses the antimeridian package to split faces along the antimeridian. More details can be found in their documentation.
You can select whether to include or exclude these antimeridian polygons by using the exclude_antimeridian parameter.
Important
For larger, higher-resolution, grids, it’s suggested to keep exclude_antimeridian=True for faster visualuzation.
(
uxds_geoflow["v1"]
.nodal_average()[0][0]
.plot.polygons(
exclude_antimeridian=False,
cmap="coolwarm",
title="With Antimeridian Faces",
width=900,
height=400,
)
+ uxds_geoflow["v1"]
.nodal_average()[0][0]
.plot.polygons(
exclude_antimeridian=True,
cmap="coolwarm",
title="Without Antimeridian Faces",
width=900,
height=400,
)
).cols(1)
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/uxarray/plot/dataarray_plot.py:480: UserWarning: Including Antimeridian Polygons may lead to visual artifacts. It is suggested to keep 'exclude_antimeridian' set to True.
warnings.warn(